{
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "\n",
    "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n",
    "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.set_loglevel(\"WARNING\")\n",
    "mpl.rcParams[\"lines.markersize\"] = 8\n",
    "\n",
    "# generate datetime string\n",
    "now = datetime.datetime.now()\n",
    "now_str = now.strftime(\"%m_%d_%H_%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "WINDOW_SIZE = 250\n",
    "\n",
    "PATH = \"...\"\n",
    "\n",
    "# Load data\n",
    "df_goal = pd.read_csv(PATH + \"perc_goal_uptime.csv\")\n",
    "df_collisions = pd.read_csv(PATH + \"perc_collisions_step.csv\")\n",
    "df_reward = pd.read_csv(PATH + \"mean_rew_step.csv\")\n",
    "\n",
    "df_goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_step = df_reward['global_step'].values\n",
    "time = df_goal['performance/uptime'].values / 3600\n",
    "\n",
    "reward = df_reward['PPO____S_1000__02_26_08_54_58_289 - metrics/mean_episode_reward_per_agent'].rolling(window=WINDOW_SIZE).mean().values\n",
    "offroad = df_collisions['PPO____S_1000__02_26_08_54_58_289 - metrics/perc_off_road'].rolling(window=WINDOW_SIZE).mean().values * 100\n",
    "collision = df_collisions['PPO____S_1000__02_26_08_54_58_289 - metrics/perc_veh_collisions'].rolling(window=WINDOW_SIZE).mean().values * 100\n",
    "goal_achieved = df_goal['PPO____S_1000__02_26_08_54_58_289 - metrics/perc_goal_achieved'].rolling(window=WINDOW_SIZE).mean().values * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1200x350 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Define constant for the number of steps to zoom in\n",
    "ZOOM_STEPS = 2000\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 3.5))\n",
    "\n",
    "# First axis (Average reward)\n",
    "axs[0].plot(time, reward, color='g', linewidth=2)\n",
    "axs[0].grid(True, color='lightgrey', alpha=0.3)\n",
    "axs[0].set_xlabel(\"Wall-clock-time [h]\", labelpad=10)\n",
    "axs[0].set_ylabel(\"Average reward\")\n",
    "axs[0].set_xlim([0, 24])\n",
    "axs[0].set_ylim([0.79, 1])\n",
    "\n",
    "# Inset for the first axis (zoom-in on last ZOOM_STEPS steps)\n",
    "axins0 = inset_axes(axs[0], width=\"30%\", height=\"35%\", loc=\"center right\")  # Slightly increased height\n",
    "axins0.plot(time[-ZOOM_STEPS:], reward[-ZOOM_STEPS:], color='g', linewidth=2)\n",
    "axins0.grid(True, color='lightgrey', alpha=0.3)\n",
    "axins0.set_xlim(time[-ZOOM_STEPS], time[-1])\n",
    "axins0.set_ylim(min(reward[-ZOOM_STEPS:]), max(reward[-ZOOM_STEPS:]))\n",
    "axins0.set_yscale('log')\n",
    "\n",
    "# Label for the zoom-in plot\n",
    "axins0.text(0.05, 1.15, 'Zoom-in', transform=axins0.transAxes, fontsize=10, color='black', ha='left', va='top')\n",
    "\n",
    "# Hide y-axis labels and ticks\n",
    "axins0.get_yaxis().set_visible(False)\n",
    "\n",
    "# Annotate the min and max values in red\n",
    "min_value = min(reward[-ZOOM_STEPS:])\n",
    "max_value = max(reward[-ZOOM_STEPS:])\n",
    "\n",
    "# Annotating min and max values\n",
    "axins0.annotate(f'{min_value:.3f}', xy=(time[-ZOOM_STEPS], min_value), xytext=(-20, -2),\n",
    "                textcoords='offset points', color='red', fontsize=10, ha='center', va='bottom')\n",
    "axins0.annotate(f'{max_value:.3f}', xy=(time[-ZOOM_STEPS], max_value), xytext=(67, 15),\n",
    "                textcoords='offset points', color='red', fontsize=10, ha='center', va='top')\n",
    "\n",
    "# Second axis (Goal achieved)\n",
    "axs[1].plot(global_step, goal_achieved, color='b', linewidth=2)\n",
    "axs[1].grid(True, color='lightgrey', alpha=0.3)\n",
    "axs[1].set_xlabel(\"Global step\", labelpad=10)\n",
    "axs[1].set_ylabel(\"Perc. goal achieved [%]\")\n",
    "\n",
    "# Inset for the second axis (zoom-in on last ZOOM_STEPS steps)\n",
    "axins1 = inset_axes(axs[1], width=\"30%\", height=\"35%\", loc=\"center right\")  # Slightly increased height\n",
    "axins1.plot(global_step[-ZOOM_STEPS:], goal_achieved[-ZOOM_STEPS:], color='b', linewidth=2)\n",
    "axins1.grid(True, color='lightgrey', alpha=0.3)\n",
    "axins1.set_xlim(global_step[-ZOOM_STEPS], global_step[-1])\n",
    "axins1.set_ylim(min(goal_achieved[-ZOOM_STEPS:]), max(goal_achieved[-ZOOM_STEPS:]))\n",
    "axins1.set_yscale('log')\n",
    "\n",
    "# Label for the zoom-in plot\n",
    "axins1.text(0.05, 1.15, 'Zoom-in', transform=axins1.transAxes, fontsize=10, color='black', ha='left', va='top')\n",
    "\n",
    "# Format y-axis labels of the inset plot\n",
    "for tick in axins1.get_yticklabels():\n",
    "    tick.set_rotation(0)\n",
    "    tick.set_fontsize(1)\n",
    "\n",
    "axins1.get_yaxis().set_visible(False)\n",
    "\n",
    "# Annotate the min and max values in red\n",
    "min_value = min(goal_achieved[-ZOOM_STEPS:])\n",
    "max_value = max(goal_achieved[-ZOOM_STEPS:])\n",
    "\n",
    "# Annotating min and max values\n",
    "axins1.annotate(f'{min_value:.3f}', xy=(global_step[-ZOOM_STEPS], min_value), xytext=(-20, -2),\n",
    "                textcoords='offset points', color='red', fontsize=10, ha='center', va='bottom')\n",
    "axins1.annotate(f'{max_value:.3f}', xy=(global_step[-ZOOM_STEPS], max_value), xytext=(67, 15),\n",
    "                textcoords='offset points', color='red', fontsize=10, ha='center', va='top')\n",
    "\n",
    "\n",
    "# Third axis (Collisions and Off-road)\n",
    "axs[2].plot(global_step, collision, color='r', label='Collided', linewidth=2)\n",
    "axs[2].plot(global_step, offroad, color='orange', label='Off-road', linewidth=2)\n",
    "axs[2].grid(True, color='lightgrey', alpha=0.3)\n",
    "axs[2].set_xlabel(\"Global step\", labelpad=10)\n",
    "axs[2].set_ylabel(\"Metric [%]\")\n",
    "#axs[2].set_yscale('log')\n",
    "axs[2].legend(facecolor='white', framealpha=1, fontsize=10)\n",
    "\n",
    "# Inset for the third axis (zoom-in on last ZOOM_STEPS steps)\n",
    "axins2 = inset_axes(axs[2], width=\"30%\", height=\"35%\", loc=\"center right\")  # Slightly increased height\n",
    "axins2.plot(global_step[-ZOOM_STEPS:], collision[-ZOOM_STEPS:], color='r', linewidth=2)\n",
    "axins2.plot(global_step[-ZOOM_STEPS:], offroad[-ZOOM_STEPS:], color='orange', linewidth=2)\n",
    "axins2.grid(True, color='lightgrey', alpha=0.3)\n",
    "axins2.set_xlim(global_step[-ZOOM_STEPS], global_step[-1])\n",
    "axins2.set_yscale('log')\n",
    "\n",
    "# Label for the zoom-in plot\n",
    "axins2.text(0.05, 1.15, 'Zoom-in', transform=axins2.transAxes, fontsize=10, color='black', ha='left', va='top')\n",
    "\n",
    "# Format y-axis labels of the inset plot\n",
    "for tick in axins2.get_yticklabels():\n",
    "    tick.set_rotation(0)\n",
    "    tick.set_fontsize(6)\n",
    "\n",
    "# Disable scientific notation for y-axis\n",
    "axins2.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{x:.2f}'))\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'training_curves.pdf', bbox_inches='tight', format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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